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2. Basic Operations on an Array
1. Arithmetic operations:
 These operations are basic arithmetic operations like
addition,subtraction,division,power etc.
 The operation takes place element wise.
 Eg: arr1=np.arange(6)
O/P:array([0, 1, 2, 3, 4, 5])
print(arr1+3)....every no. is added by 3
print(arr1*2).... every no. is multiplied by 2
print(arr1/3).... every no. is divided by 3
 Hence element wise operations are performed whereas in the list
it will repeat the same list .
 Eg: lst=[10,20,30]
print(lst*3)
 This will repeat the list thrice.
O/P: =[10,20,30,10,20,30,10,20,30]

3. Arithmetic operations between 2
Arrays
 Even between two arrays the operations take
place element wise.
 Eg 1 : arr1=np.arange(6).....[0,1,2,3,4,5]
arr2=np.arange(6) ).....[0,1,2,3,4,5]
print(arr1+arr2)
O/P: [ 0 2 4 6 8 10]
 Eg 2: print(arr1>arr2)
Here element wise comparison takes place and
returns array of boolean values.As the values in
arr1 and arr2 are equal it will return an array of
boolean values.
 O/P: [False False False False False False]

4. NOTE
 If number of elements in both the arrays are not same then it will throw an error .
Hence in this case as the broadcasting is not possible number of elements in the array
needs to be equal.
 In case of scalar multiplication element wise multiplication takes place while in vector
product dot product is considered . Hence number of rows should be equal to number
of columns.
 Eg:2D array: arr1=np.arange(6).reshape(2,3)
arr2=np.arange(7,13).reshape(2,3)
arr1=array([[ 1,2,3], [4, 5, 6]])
arr2=array([[ 7, 8, 9], [10, 11, 12]])
 np.dot(arr1,arr2) OR print(arr1.dot(arr2)
 O/P: array([[ 31, 34], [112, 124]])
 In all these examples every time new array is created and the original array remains as
it is.So the original array is not discarded.

5. 2.Update/Modify the existing
array
 When an operation is performed on the array
always new array is created and original
remains as it is.To make changes in the
orignal array we use the below methd.
 arr=np.array([10,20,30])
arr+=2............# arr=arr+2
print(arr)
 O/P: [12 22 32]
 This will make changes to the existing array.

6. 3.Unary Operations
 A single operator or unary operation
is one which takes and performs an operation with a single
operand / argument.
 Aggregation functions such as min(),max(),etc also used to
perform various operations on an array.
 Let arr1=[10,20,30]
1. sum():finding sum of all the elements in the array.
arr1.sum()........O/P:60
2.min(): finding minimum value from all the elements in
the array
arr1.sum()........O/P:10
3.max(): finding maximum value from all the elements in
the array
arr1.sum()........O/P:30

7. Finding the aggregation of
elements along row or column
 These functions can also be used to find the values
along the rows or columns.
 Eg: arr1=np.arange(12).reshape(6,2)
arr1
 O/P: array([[ 0, 1], [ 2, 3], [ 4, 5], [ 6, 7], [ 8, 9], [10,
11]])
np.sum(arr1,axis=0/1)
np.max(arr1,axis=0/1)
np.min(arr1,axis=0/1)
axis=0 :column wise addition/min/max value
axis=1:row wise addition/min/max value

8. Universal Functions
MATHS FINCTIONS
 np.add()-adds values
np.add(2,3)......O/P: 5
 np.multiply()-multiplication of the values
np.multiply(2,3)......O/P: 6
 np.sqrt()-to find square root of elements in array
np.sqrt(9,16) )......O/P: 3,4
 np.log()-to find log of a value
np.log(1) .....O/P: 0.0
 np.square()-to find square of a value
np.log(5) .....O/P: 25

9. Trignometric functions
 np.sin/cos/tan(angle)-to find value of specific angle
np.sin (np.pi/2) .....O/P: 1.0
 To convert values into angles that is, to understand that
it is value in degrees multiply the angle by pi/180 else it
will consider them as usual numerical values.
 Eg:
np.sin(np.array([0,30,60,90,120]))*(np.pi/180)
O/P: array([ 0. , -0.0172444 , -0.00531995, 0.01560319,
0.01013358])
 To converting an array of values of angles to array
values in radians use np.radians(angles).
 To find inverse trignometric functions use
arcsin(),arccos(),arctan() functions

10. Bitwise operators
 and (&): (int,int) -> int :0011 & 0101 returns the result 0001 inclusive
 Or (|): (int,int) -> int: 0011 & 0101 returns the result 0111
 not (~): (int) -> int: ~01 returns the result 10
 exclusive or(^): (int,int) -> int :0011 & 0101 returns the result 0110
 shift left (<<): (int,int) -> int: 101 << 2 returns the result 10100
For shifting left, 0s are added on the right
 shift right (>>): (int,int) -> int: 101 >> 2 returns the result 1
for shifting right, bits are removed from the right
 Examples: print(np.bitwise_and(10,20))
print(np.bitwise_or(10,20))
 To get the binary representation use below method:
print(np.binary_repr(30))........O/P: 11110

11. Statistical functions
 arr_pop=np.array([200,300,707,505,50,800])
 Mean:
finding mean of elements in the array
print(np.mean(arr_pop)).......O/P:427.0
 Median:
finding median of elements in the array
print(np.median(arr_pop)) ......O/P:402.5
 Standard deviation:
finding standard deviation of elements in the array
print(np.std(arr_pop)) ......O/P:268.762
 Variance:
finding variance of elements in the array
print(np.var(arr_pop)) ......O/P:72233.333